In order to solve the problems of domestic and foreign vegetable precision metering devices that easily damage seeds and have low seeding efficiency and poor qualification rate, this paper designs a one-machine for two-row pneumatic combination seeder, which is mainly composed of a pneumatic seeding mechanism and a honeycomb seeding mechanism. The mechanism and other components and theoretical analysis of the working process of the combined seed metering device clarifies the minimum negative pressure for adsorbing a seed during the suction stage, along with the negative pressure required for the seed-carrying stage. It also establishes the seeding speed and movement during planting with the trajectory parameter equation and uses ADAMS software to conduct virtual experiments on the influence of the number of holes, diameters, and heights of different types of honeycomb seeding disks on the success rate of seeding. Based on research on previous pneumatic seeding mechanisms, single factor tests, orthogonal tests, and high-speed photography tests of the honeycomb seeding disk of the combined seeder were carried out on the number of holes, the diameter of holes, and the height of seeding. The results of tests conducted on okra seeds showed that when the rotating speed of the pneumatic seeding disc was 18 r/min, the diameter of the pneumatic seeding disc hole was 2.4 mm, the vacuum degree was 3.5 kPa, the number of honeycomb seeding disc holes was 24, the hole diameter was 13 mm, and the seed height was 60 mm, the working performance of the combined seed meter reached the optimal level, which provides a reference for the development of a small seed vegetable precision seeder.

Crop diseases have an important impact on the safe production of food. Therefore, the automated identification of pre-crop diseases is very important for farmers to increase production and income. In this paper, a tomato leaf disease identification method based on the optimized MobileNetV2 model is proposed. A dataset of 20,400 tomato disease images was created based on tomato disease images taken from the greenhouse and obtained from the PlantVillage database. The optimized MobileNetV2 model was trained with the dataset to obtain a classification model for tomato leaf diseases. The average recognition accuracy of the model is 98.3% and the recall rate is 94.9%, which is 1.2% and 3.9% higher than the original model, respectively, after experimental validation. The average prediction speed of the model for a single image is about 76 ms, which is 2.94% better than the original model. To verify the performance of the optimized MobileNetV2 model, it was compared with the Xception, Inception, and VGG16 feature extraction network models using migration learning, respectively. The experimental results show that the average recognition accuracy of the model is 0.4 to 2.4 percentage points higher than that of the Xception, Inception, and VGG16 models. It can provide technical support for the identification of tomato diseases, and is also important for plant growth monitoring under precision agriculture.